CDM reference backends

Overview

The CDMConnector package allows us to work with cdm data in different locations consistently. The cdm_reference may be to tables in a database, files on disk, or tables loaded into R. This allows computation to take place wherever is most convenient.

Here we have a schematic of how CDMConnector can be used to create cdm_references to different locations.

Example

To show how this can work (and slightly overcomplicate things to show different options), let´s say we want to create a histogram with age of patients at diagnosis of tear of meniscus of knee (concept_id of “4035415”). We can start in the database and, after loading the required packages, subset our person table people to only include those people in the condition_occurrence table with condition_concept_id “4035415”

library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)

con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
cdm <- cdm_from_con(con, cdm_name = "eunomia", cdm_schema = "main", write_schema = "main")

# first filter to only those with condition_concept_id "4035415"
cdm$condition_occurrence %>% tally()

cdm$condition_occurrence <- cdm$condition_occurrence %>%
  filter(condition_concept_id == "4035415") %>%
  select(person_id, condition_start_date)

cdm$condition_occurrence %>% tally()

# then left_join person table
cdm$person %>% tally()
cdm$condition_occurrence %>%
  select(person_id) %>%
  left_join(select(cdm$person, person_id, year_of_birth), by = "person_id") %>% 
  tally()

We can save these tables to file

dOut <- tempfile()
dir.create(dOut)
CDMConnector::stow(cdm, dOut, format = "parquet")

And now we can create a cdm_reference to the files

cdm_arrow <- cdm_from_files(dOut, as_data_frame = FALSE, cdm_name = "GiBleed")

cdm_arrow$person %>%
  nrow()

cdm_arrow$condition_occurrence %>%
  nrow()

And create an age at diagnosis variable

result <- cdm_arrow$person %>%
  left_join(cdm_arrow$condition_occurrence, by = "person_id") %>%
  mutate(age_diag = year(condition_start_date) - year_of_birth) %>%
  collect()

We can then bring in this result to R and make the histogram

str(result)

result %>%
  ggplot(aes(age_diag)) +
  geom_histogram()

DBI::dbDisconnect(con, shutdown = TRUE)